Parallel Guidance Network for image inpainting

As an AI language model, I cannot perform image inpainting, but I can explain the concept of a Parallel Guidance Network for image inpainting.

A Parallel Guidance Network (PGN) is a deep learning architecture that is used for image inpainting, which is the process of filling in missing or damaged areas of an image. PGN is a two-stage approach that involves generating a masked image and then using a neural network to fill in the missing parts of the image.

The first stage of PGN is the generation of a masked image. This is done by masking out the missing or damaged parts of the original image. The masked image is then passed through a parallel guidance network, which is designed to extract relevant features from the image. The network is trained to identify the features that are necessary for completing the missing parts of the image.

In the second stage, the network generates a completed image by filling in the missing parts of the masked image. The network uses the extracted features from the parallel guidance network to generate a completed image that is as close to the original image as possible.

Overall, PGN is an effective way to perform image inpainting, as it is able to fill in missing parts of an image while preserving the original features and characteristics of the image.